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As technology has made data increasingly accessible, data science continues to transform and shape how we think about problems across various industries. Banking has a vast number of applications for predictive and prescriptive analytics as data flows in through point-of-sale transactions, deposits/withdrawals, customer profiling (KYC), your own CRM, and other external curated data sources. Regardless of where your organization sits, data science can help you solve a number of problems. Today we will be taking a look at a few use cases for data science in the banking industry.

data science in banking industry

Applications of Data Science


Many banks offer a suite of products and services to meet their customers' needs. These can include deposits, consumer/ corporate lending, investments, credit, and more. Anticipating the products and services your customers need is pivotal in increasing your percentage of wallet share with existing customers. Predictive modeling is a cutting-edge data science approach to identify and anticipate customer need prior to the customer's own awareness of their needs.

Predictive analytics identify current and historical behaviors in products and services that a customer is already subscribed to. It then complements behavior with other factors like the customer's profile, credit situation, and more. Effectively, the predictive model takes this one unique customer and compares it with other similar customers in the past who acted on cross-selling opportunities. The result is a data science-sourced actionable ranking of the probability that a customer will respond to the cross-selling of a product. This enables your team to focus on nurturing customer relationships efficiently while prioritizing marketing efforts.


Fraud Detection

Preventing fraud directly impacts customer trust and loyalty, as well as affecting the bottom line with your credit or debit card products. According to the Federal Reserve, new technologies and best practices, like embedding microchips in credit and debit cards, have effectively reduced point-of-sale thefts. Adversely, criminals have reacted by transitioning to online purchase theft with your customer’s credit or debit products. This increase in card-not-present fraud introduces a problem for banking as it reduces the ability of users to identify fraud early. That early detection is an important factor in supporting the bank’s anti-fraud efforts. 

Fraud detection modeling is the art of capturing the nuance of customer behaviors to efficiently distinguish cases of potential fraud from the everyday mundane behaviors of your customers. Fraud detection models, including K-means clustering and deep learning models, organize individual factors like the location of purchase, time of day, historical customer trends, and more, to determine the likelihood that a purchase was out of the realm of normal for any specific customer. These data science-informed systems, when implemented correctly, save banks and customers from frustration, fraud, and the loss of large amounts of money. According to the American Banking Association, America’s banks prevented $22.3 billion worth of attempted deposit account theft, in part using these anti-fraud data science systems.

Fraudsters are smart and recognize these methods are deployed by large providers like CapitalOne, Citi Bank and adjust their behaviors to avoid triggering fraud detection systems by closely mimicking the behavior of their victims. Because of this, it is of the utmost importance when considering fraud detection solutions to choose systems that are cutting edge, constantly evolving, and are supported well so they can properly and efficiently react to your data and maintain the edge in fraud’s cat and mouse game. This is why data science and analytics are crucial in fraud detection.

Banks Stop $22.3 Billion in Fraud Attempts in 2018:

Survey: 33 Million Americans May Have Been Card Fraud Victims Last Year:


Data science and customer retention in banking

Retention Management

A bank faces expenses related to the retention of two groups of people: customers and employees. According to Forbes, North American banks face an annual attrition rate of 11% for customers. Knowing the traits that cause turnover, or at least identifying people who are likely to leave early, can provide tremendous value in minimizing attrition risk. Some banks are using a suite of advanced analytics tools to provide information unique to your customers' experience. For example, using Natural Language Processing (NLP) on call center logs. This process breaks transcripts down to keywords and sentiment to gauge common issues and the intensity at which the bank may lose a customer.

The use of these advanced analytics tools to gather information on the customer, in conjunction with explainable predictive modeling, can provide you the means to identify and act on customers who are likely to leave. In practice, your customer manager would see a list of customers most likely to leave and could access a list of reasons as to why the customer was predicted to leave (ex. The customer's recent call mentioned “Customer Service” as an issue). The same principles can be applied to employee retention.

Why Retaining Customers For Banks Is As Important As Winning New Ones:


Personalized Marketing

Effective marketing is a blend of the right message, at the right time, in the right channel. Failure on any part greatly reduces the likelihood of the conversion of your marketing dollars. Personalized marketing means building 1-on-1 messaging for each possible customer. The message matters. For example, marketing loans for real estate listings to customers who do not have the means is a waste of money. The right time also matters. Is the customer intending to purchase the home now? If not, the bank can reduce the amount of awareness marketing to remain effective and top of mind. Equally, the place you market to the customer matters in their level of trust, interaction, and intent to act on the advertisement.

You can use data science across each of these three domains (message, time, and channel) to refocus marketing budgets to higher ROI opportunities. Messaging can be tested using A/B testing to determine which advertisements see the highest lift in ROI. Trends and forecasting can give you an indication of when advertisement campaigns should be prioritized. Again, A/B testing can tell you where the best channels for particular people are to increase the likelihood of success in conversion.

Utilizing Data Science & AI to Personalize the Customer Journey:


data science and predictive analytics in banking

Customer Lifetime Value (CLV)

Valuing customers beyond their expected return in the current year can help a bank prioritize marketing, focus efforts on communication, and reduce attrition of important clients. Customer lifetime valuing (CLV) is the art of anticipating the expected return of a customer for a set window of time. The purpose is to evaluate the expected interest, service fees, and investments your bank is expected to generate from any customer beyond their current value to the bank. CLV can be used as a metric to understand long-term health and guide strategy development in pursuit of growth.

As you can imagine, reliably estimating a customer’s lifetime value is quite difficult. This difficulty arises when recognizing which behaviors or characteristics indicate the revenue growth of a customer, and it becomes even trickier to determine by how much. This task is impossibly difficult for humans, however, computers using regression models on historical behaviors and the characteristics of customers to estimate revenue at a future point in time can provide a reliable estimate. These methods are most accurate when your services and products have remained and will continue to remain constant for the foreseeable future.


Risk Modeling

Credit risk modeling is important in intervention with customers who may cause loss to the bank. However, risk modeling is not as simple as understanding the liquidity of the customer. For one, it is difficult to compare customers (ex. customers from different industries). The last thing you want to do is host premature sanctions on good customers causing them to move to competitors.

You can use explainable regression models to determine a probability of default (POD) based on credit reports, banking behavior, customer characteristics, and more. The probabilities are based on defaults of past customers. Explainability of the model is particularly useful in informing credit officers or relationship managers as to why one customer, similar to another, received a more risky score, better informing next steps to reduce the risk of the customer.

Credit Risk Analysis Models:



Although this blog has reviewed a few high-level concepts and applications of data science in the banking industry, the opportunities are limitless. The foundation of a good model is almost always grounded by a question.

If you have questions, there is a high possibility that data science can be used to help facilitate your business!


Further Resources

Fraud and Anomaly Detection in Banking:

Data Science for Banking and Financial Industry | Data Science Careers in 2020 | Edureka:

Leda Braga: Data science and its role in investment strategy:

Machine Learning in Banking – Opportunities, Risks, Use Cases:



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